Faculty Bibliography

On September 3^rd, 2020, the Collaborative Community on Ophthalmic Imaging (CCOI) conducted its first two-day virtual workshop on the role of artificial intelligence (AI) and related machine learning (ML) techniques for the diagnosis and treatment of various ophthalmic conditions. In a session entitled, "Artificial Intelligence For Glaucoma", a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities including fundus photography, optical coherence tomography imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations and challenges to its real-world clinical application. The panelists' discussion addressed AI and health equities from the clinical, societal and regulatory perspectives.

PURPOSE/OBJECTIVE:To investigate the costs and effects of implanting trabecular meshwork bypass stents during cataract surgery from a societal perspective in the United States. DESIGN/METHODS:Cost-utility analysis using Markov models and efficacy/safety data from published pivotal or randomized control trials (RCTs) of devices investigated. PARTICIPANTS/METHODS:Patients aged 65 years and older with mild to moderate primary open-angle glaucoma with or without visually significant cataract. METHODS:With the use of Markov models, glaucoma progression through 4 glaucoma states (mild, moderate, advanced, severe/blind) and death were simulated over 35 years. The cohort with cataract entered the model and received cataract surgery with or without device implantation. We included a medication management only reference group to calculate total costs and outcomes for those without cataract. Intraocular pressure (IOP) reductions from RCTs were converted to glaucoma state transition probabilities using visual field (VF) mean deviation (MD) decline rates from the Early Manifest Glaucoma Trial. Progressive thinning of the retinal nerve fiber layer (RNFL) on OCT imaging related to IOP control warranted further intervention, including adding medication, selective laser trabeculoplasty (SLT), or incisional glaucoma surgery. We estimated whole costs at Medicare rates and obtained utility values for glaucoma states from previous studies. Incremental costs per quality-adjusted life-year (QALY) gained were evaluated at a QALY threshold of $50 000. One-way deterministic sensitivity analysis, scenario analyses, and probabilistic sensitivity analyses addressed parameter uncertainty and demonstrated model robustness. MAIN OUTCOME MEASURES/METHODS:Total costs, QALY, and incremental cost-effectiveness ratio (ICER). RESULTS:Over 35 years in the base case, the Hydrus (Ivantis, Inc.) implanted with cataract surgery arm cost $48 026.13 and gained 12.26 QALYs. The iStent inject (Glaukos Corp.) implanted with cataract surgery arm cost $49 599.86 and gained 12.21 QALYs. Cataract surgery alone cost $54 409.25 and gained 12.04 QALYs. Initial nonsurgical management cost $57 931.22 and gained 11.74 QALY. The device arms dominated or were cost-effective compared with cataract surgery alone within 5 years and throughout sensitivity analyses. The iStent inject arm was cost-effective in 94.19% of iterations in probabilistic sensitivity analyses, whereas the Hydrus arm was cost-effective in 94.69% of iterations. CONCLUSIONS:Implanting the Hydrus Microstent or iStent inject during cataract surgery is cost-effective at a conservative QALY threshold.

We hypothesize that artificial intelligence applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community for Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community in Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was one of the first two such organizations officially designated by the FDA in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, as access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of artificial intelligence technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert level evaluation to individuals even in the most remote regions of the planet.

The last three decades have been a turning point for early glaucoma diagnosis and monitoring by newly developed imaging techniques. Improvements in imaging of the anterior segment (AS), retinal nerve fiber layer (RNFL), optic nerve head (ONH), and macular ganglion cell complex (GCC) came into existence and broke new ground for early diagnosis and progression survey. This review will summarize the different ophthalmic imaging techniques, their principles, pros and cons, ongoing development processes, and the future of imaging methods. The aim is to provide the clinician with an understanding of the role of imaging methods and provide insight for appropriate use and evaluation of their outputs in glaucoma diagnosis and treatment. This article is protected by copyright. All rights reserved.